Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
○Academic publication links
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✓Committers with academic emails
1 of 4 committers (25.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.9%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: pmgrollemund
- Language: HTML
- Default Branch: master
- Size: 54.2 MB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 5
Metadata Files
README.md
bliss 
Bayesian functional Linear regression with Sparse Step functions (BLiSS)
A method for the Bayesian Functional Linear Regression model (functions-on-scalar), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available.
https://pmgrollemund.github.io/bliss/
Installation
To install the bliss package, the easiest is to install it directly from GitHub. Open an R session and run the following commands:
R
library(remotes)
install_github("pmgrollemund/bliss", build_vignettes=TRUE)
Usage
Once the package is installed on your computer, it can be loaded into a R session:
R
library(bliss)
help(package="bliss")
Citation
As a lot of time and effort were spent in creating the bliss method, please cite it when using it for data analysis:
Grollemund, Paul-Marie; Abraham, Christophe; Baragatti, Meïli; Pudlo, Pierre. Bayesian Functional Linear Regression with Sparse Step Functions. Bayesian Anal. 14 (2019), no. 1, 111--135. doi:10.1214/18-BA1095. https://projecteuclid.org/euclid.ba/1524103229
You should also cite the bliss package:
R
citation("bliss")
See also citation() for citing R itself.
Owner
- Login: pmgrollemund
- Kind: user
- Repositories: 2
- Profile: https://github.com/pmgrollemund
GitHub Events
Total
Last Year
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| sanchezi | i****z@i****r | 54 |
| pmgrollemund | p****d@u****r | 24 |
| pmgrolle | p****d@u****r | 9 |
| sanchezi | i****z@i****r | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
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Packages
- Total packages: 1
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Total downloads:
- cran 428 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 8
- Total maintainers: 1
cran.r-project.org: bliss
Bayesian Functional Linear Regression with Sparse Step Functions
- Homepage: https://github.com/pmgrollemund/bliss
- Documentation: http://cran.r-project.org/web/packages/bliss/bliss.pdf
- License: GPL-3
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Latest release: 1.1.1
published almost 2 years ago
Rankings
Maintainers (1)
Dependencies
- R >= 3.3.0 depends
- MASS * imports
- Rcpp * imports
- RcppArmadillo * imports
- RColorBrewer * suggests
- knitr * suggests
- rmarkdown * suggests
- R >= 3.3.0 depends
- MASS * imports
- Rcpp * imports
- rockchalk * imports
- RColorBrewer * suggests
- knitr * suggests
- rmarkdown * suggests
- R >= 3.3.0 depends
- MASS * imports
- Rcpp * imports
- rockchalk * imports
- RColorBrewer * suggests
- knitr * suggests
- rmarkdown * suggests